Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices
This work addresses efficiency challenges in biomedical network analysis, such as drug-repurposing for COVID-19, though it appears incremental as it builds on existing knowledge graph methods with a new representation.
The paper tackled the complexity of biomedical knowledge graphs by introducing Prime Adjacency Matrices (PAM), a representation framework that uses prime numbers to enable efficient network analysis, achieving better results than existing workflows with simple, training-free methods in less time.
Most phenomena related to biomedical tasks are inherently complex, and in many cases, are expressed as signals on biomedical Knowledge Graphs (KGs). In this work, we introduce the use of a new representation framework, the Prime Adjacency Matrix (PAM) for biomedical KGs, which allows for very efficient network analysis. PAM utilizes prime numbers to enable representing the whole KG with a single adjacency matrix and the fast computation of multiple properties of the network. We illustrate the applicability of the framework in the biomedical domain by working on different biomedical knowledge graphs and by providing two case studies: one on drug-repurposing for COVID-19 and one on important metapath extraction. We show that we achieve better results than the original proposed workflows, using very simple methods that require no training, in considerably less time.